{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                     \n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Velocity Profiles for SIMPLE Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1193e6828>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_20 = [0.000000,-0.019275,-0.036197,-0.051383,-0.065310,-0.078341,-0.090740,-0.102693,-0.114311,-0.125633,-0.136628,-0.147189,-0.157126,-0.166159,-0.173904,-0.179864,-0.183413,-0.183790,-0.180081,-0.171219,-0.155978,-0.132981,-0.100722,-0.057599,-0.001978,0.067722,0.152904,0.254631,0.373434,0.509096,0.660446,0.825211,1.000000]\n",
    "y_20 = np.linspace(0, 1, len(u_20))\n",
    "plt.plot(u_20, y_20, label = \"20\")\n",
    "\n",
    "u_100 = [0, -0.0194223816917762, -0.0364794462753162, -0.0517972884455051, -0.0658574586747664, -0.0790225483797341,\n",
    "-0.0915537052111879, -0.103621354540349, -0.115310421128771, -0.126621393337052, -0.137468633462998, -0.14767741301864, -0.174364569550853, -0.1678564847174, -0.156395382236027, -0.139220153535652, -0.115522772877862\n",
    ", -0.0843988533752021, -0.0447612114385153, 0.00478067381636638, 0.0660527988829808, 0.141405407431201\n",
    ", 0.233659514297034, 0.345769017375264, 0.480043554799291, 0.636848639181875, 0.812930591555768\n",
    ", 1]\n",
    "y_100 = np.linspace(0, 1, len(u_100))\n",
    "plt.plot(u_100, y_100, label = \"100\")\n",
    "\n",
    "u_200 = [0.000000,-0.020362,-0.038322,-0.054537,-0.069506,-0.083597,-0.097056,-0.110013,-0.122477,-0.134333,-0.145341,-0.155135,-0.163244,-0.169110,-0.172128,-0.171701,-0.167295,-0.158498,-0.145068,-0.126955,-0.104284,-0.077278,-0.046116,-0.010699,0.029676,0.076759,0.133857,0.206254,0.301175,0.426632,0.588406,0.784783,1.000000]\n",
    "y_200 = np.linspace(0, 1, len(u_200))\n",
    "plt.plot(u_200, y_200, label = \"200\")\n",
    "\n",
    "plt.xlabel(\"u-velocity\")\n",
    "plt.ylabel(\"y-position\")\n",
    "plt.title(\"SIMPLE: u Along Central Vertical Axis\")\n",
    "plt.legend(title=\"Reynold's #\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1194d6f98>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_20 = [0.000000,0.044296,0.081689,0.111679,0.134150,0.149305,0.157585,0.159592,0.156015,0.147577,0.134986,0.118911,0.099971,0.078728,0.055696,0.031355,0.006165,-0.019409,-0.044880,-0.069712,-0.093305,-0.114979,-0.133962,-0.149397,-0.160361,-0.165895,-0.165072,-0.157069,-0.141266,-0.117346,-0.085388,-0.045933,0.000000]\n",
    "x_20 = np.linspace(0, 1, len(v_20))\n",
    "plt.plot(x_20, v_20, label = \"20\")\n",
    "\n",
    "v_100 = [0.000000,0.041837,0.076190,0.103275,0.123546,0.137591,0.146054,0.149567,0.148708,0.143976,0.135783,0.124460,0.110265,0.093413,0.074092,0.052494,0.028848,0.003444,-0.023321,-0.050919,-0.078644,-0.105588,-0.130619,-0.152378,-0.169317,-0.179787,-0.182191,-0.175198,-0.158007,-0.130587,-0.093820,-0.049476,0.000000]\n",
    "x_100 = np.linspace(0, 1, len(v_100))\n",
    "plt.plot(x_100, v_100, label = \"100\")\n",
    "\n",
    "v_200 = [0.000000,0.041942,0.075381,0.101161,0.120243,0.133591,0.142084,0.146458,0.147284,0.144961,0.139736,0.131727,0.120958,0.107393,0.090968,0.071629,0.049361,0.024235,-0.003551,-0.033613,-0.065324,-0.097739,-0.129527,-0.158914,-0.183667,-0.201165,-0.208625,-0.203547,-0.184394,-0.151337,-0.106686,-0.054585,0.000000]\n",
    "x_200 = np.linspace(0, 1, len(v_200))\n",
    "plt.plot(x_200, v_200, label = \"200\")\n",
    "\n",
    "plt.ylabel(\"v-velocity\")\n",
    "plt.xlabel(\"x-position\")\n",
    "plt.title(\"SIMPLE: v Along Central Horizontal Axis\")\n",
    "plt.legend(title=\"Reynold's #\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Runtime Plots for SIMPLE Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x, A, B): # this is your 'straight line' y=f(x)\n",
    "    return A*x + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'log(Runtime)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Runtime to achieve convergence of 10e-4 tolerance\n",
    "times = np.array([0.6592, 0.8282, 0.9746, 1.2447, 1.7277, 2.9254, 2.9254, 7.7381, 11.8378, 17.8102\n",
    ", 17.8102, 39.0394, 71.4644, 90.1572, 126.2397])\n",
    "# Dimension of Grid\n",
    "n_vals = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150])\n",
    "plt.plot(n_vals, np.log(times), 'ro')\n",
    "slope, intercept = curve_fit(f, n_vals, np.log(times))[0] \n",
    "x_vals = np.linspace(0, 160, 320)\n",
    "y_vals = f(x_vals, slope, intercept)\n",
    "plt.plot(x_vals, y_vals)\n",
    "plt.title(\"SIMPLE: Runtime vs. Mesh Size\")\n",
    "plt.xlabel(\"Mesh Grid Dimension n\")\n",
    "plt.ylabel(\"log(Runtime)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
